Prediction of G protein-coupled receptor encoding sequences from the synganglion transcriptome of the cattle tick, Rhipicephalus microplus.

The cattle tick, Rhipicephalus (Boophilus) microplus, is a pest which causes multiple health complications in cattle. The G protein-coupled receptor (GPCR) super-family presents a candidate target for developing novel tick control methods. However, GPCRs share limited sequence similarity among orthologous family members, and there is no reference genome available for R. microplus. This limits the effectiveness of alignment-dependent methods such as BLAST and Pfam for identifying GPCRs from R. microplus. However, GPCRs share a common structure consisting of seven transmembrane helices. We present an analysis of the R. microplus synganglion transcriptome using a combination of structurally-based and alignment-free methods which supplement the identification of GPCRs by sequence similarity. TMHMM predicts the number of transmembrane helices in a protein sequence. GPCRpred is a support vector machine-based method developed to predict and classify GPCRs using the dipeptide composition of a query amino acid sequence. These two bioinformatic tools were applied to our transcriptome assembly of the cattle tick synganglion. Together, BLAST and Pfam identified 85 unique contigs as encoding partial or full length candidate cattle tick GPCRs. Collectively, TMHMM and GPCRpred identified 27 additional GPCR candidates that BLAST and Pfam missed. This demonstrates that the addition of structurally-based and alignment-free bioinformatic approaches to transcriptome annotation and analysis produces a greater collection of prospective GPCRs than an analysis based solely upon methodologies dependent upon sequence alignment and similarity.